Update publications.html

publications.html

@@ -162,7 +162,7 @@ <h4 class="text-left">Journal Papers</h4>
               <div class="periodical"><strong><font color="#3B57D3">IEEE Transactions on Robotics</font>
               </strong>, 2023.</div>
               <div class="links"><a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a>
-                <a href="https://arxiv.org/abs/2207.13255" class="btn btn-sm z-depth-0"
+                <a href="https://ieeexplore.ieee.org/abstract/document/10288223" class="btn btn-sm z-depth-0"
                    role="button" target="_blank" rel="noopener noreferrer">PDF</a> <a
                         href="https://www.youtube.com/watch?v=tluvENcWldw"
                         class="btn btn-sm z-depth-0" role="button" target="_blank"
@@ -336,7 +336,7 @@ <h4 class="text-left">Conference Papers</h4>
               <div class="author"><strong><font color="#A0522D">A.D. Saravanos</font></strong>, Y.
                 Li and E.A. Theodorou
               </div>
-              <div class="periodical"><strong><font color="#3B57D3">Robotics: Science and Systems</font></strong>, 2023.</div>
+              <div class="periodical"><strong><font color="#3B57D3">Robotics: Science and Systems (RSS)</font></strong>, 2023.</div>
               <div class="links"><a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a>
                 <a href="http://www.roboticsproceedings.org/rss18/p055.html"
                    class="btn btn-sm z-depth-0" role="button" target="_blank"
@@ -375,8 +375,7 @@ <h4 class="text-left">Conference Papers</h4>
           <div>
             <div class="col-sm-2 abbr"><abbr class="badge" style="width: 120px;">IROS 2024 </abbr></div>
             <div id="saravanos2022distributed_mpcs">
-              <div class="title"><font size="+1">Distributed Model Predictive Covariance
-                Steering</font></div>
+              <div class="title"><font size="+1">Improved Exploration for Safety-Embedded Differential Dynamic Programming Using Tolerant Barrier States</font></div>
               <div class="author">J.E. Kuperman, H. Almubarak, <strong><font color="#A0522D">A.D. Saravanos</font></strong> and E.A. Theodorou
               </div>
               <div class="periodical"><strong><font color="#3B57D3">International Conference on Advanced Robotics (ICAR)</font></strong>, 2023.
@@ -387,23 +386,15 @@ <h4 class="text-left">Conference Papers</h4>
                         href="https://www.youtube.com/watch?v=9ZRBHZfjKPY"
                         class="btn btn-sm z-depth-0" role="button" target="_blank"
                         rel="noopener noreferrer">Video</a></div>
-              <div class="abstract hidden"><p>This paper proposes Distributed Model Predictive
-                Covariance Steering (DMPCS), a novel method for safe multi-robot control under
-                uncertainty. The scope of our approach is to blend covariance steering theory,
-                distributed optimization and model predictive control (MPC) into a single
-                methodology that is safe, scalable and decentralized. Initially, we pose a problem
-                formulation that uses the Wasserstein distance to steer the state distributions of a
-                multi-robot team to desired targets, and probabilistic constraints to ensure safety.
-                We then transform this problem into a finite-dimensional optimization one by
-                utilizing a disturbance feedback policy parametrization for covariance steering and
-                a tractable approximation of the safety constraints. To solve the latter problem, we
-                derive a decentralized consensus-based algorithm using the Alternating Direction
-                Method of Multipliers (ADMM). This method is then extended to a receding horizon
-                form, which yields the proposed DMPCS algorithm. Simulation experiments on
-                large-scale problems with up to hundreds of robots successfully demonstrate the
-                effectiveness and scalability of DMPCS. Its superior capability in achieving safety
-                is also highlighted through a comparison against a standard stochastic MPC
-                approach.</p></div>
+              <div class="abstract hidden"><p>In this paper, we introduce Tolerant Discrete Barrier States (T-DBaS), a novel safety-embedding technique 
+                for trajectory optimization with enhanced exploratory capabilities. The proposed approach generalizes the standard discrete barrier state (DBaS) 
+                method by accommodating temporary constraint violation during the optimization process while still approximating its safety guarantees. Consequently, 
+                the proposed approach eliminates the DBaS's safe nominal trajectories assumption, while enhancing its exploration effectiveness for escaping local minima. 
+                Towards applying T-DBaS to safety-critical autonomous robotics, we combine it with Differential Dynamic Programming (DDP), leading to the proposed 
+                safe trajectory optimization method T-DBaS-DDP, which inherits the convergence and scalability properties of the solver. The effectiveness of the 
+                T-DBaS algorithm is verified on differential drive robot and quadrotor simulations. In addition, we compare against the classical DBaS-DDP as well as 
+                Augmented-Lagrangian DDP (AL-DDP) in extensive numerical comparisons that demonstrate the proposed method's competitive advantages. Finally, the applicability 
+                of the proposed approach is verified through hardware experiments on the Georgia Tech Robotarium platform.</p></div>
             </div>
           </div>
         </li>
@@ -416,7 +407,7 @@ <h4 class="text-left">Conference Papers</h4>
                 Control using Deep FBSDEs and ADMM</font></div>
               <div class="author"> M.A. Pereira*, <strong><font color="#A0522D">A.D. Saravanos*</font></strong>, O. So, and E.A. Theodorou
               </div>
-              <div class="periodical"><strong><font color="#3B57D3">Robotics: Science and Systems </font>
+              <div class="periodical"><strong><font color="#3B57D3">Robotics: Science and Systems (RSS)</font>
               </strong>, 2022.</div>
               <div class="links"><a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a>
                 <a href="http://www.roboticsproceedings.org/rss18/p055.html"
@@ -483,7 +474,7 @@ <h4 class="text-left">Conference Papers</h4>
               <div class="author"><strong><font color="#A0522D">A.D. Saravanos</font></strong>, A.
                 Tsolovikos, E. Bakolas, and E.A. Theodorou
               </div>
-              <div class="periodical"><strong><font color="#3B57D3">Robotics: Science and Systems</font>
+              <div class="periodical"><strong><font color="#3B57D3">Robotics: Science and Systems (RSS)</font>
               </strong> , 2021.</div>
               <div class="links"><a class="abstract btn btn-sm z-depth-0" role="button">Abstract</a>
                 <a href="http://www.roboticsproceedings.org/rss17/p075.html"